论文标题
具有神经时空模型的动态结构化照明显微镜
Dynamic Structured Illumination Microscopy with a Neural Space-time Model
论文作者
论文摘要
结构化照明显微镜（SIM）从具有不同的照明模式捕获的多个原始图像中重建超级分辨图像；因此，采集速度有限，使其不适合动态场景。我们提出了一种新方法Speckle Flow SIM，该方法使用移动样品使用静态图案照明，并在数据捕获过程中对样品运动进行建模，以便用超分辨率重建动态场景。 Speckle Flow SIM依靠样品运动来捕获一系列原始图像。动态场景的时空关系是使用具有基于坐标的多层感知器（MLP）的神经时空模型建模的，并共同恢复了运动动力学和超级分辨的场景。我们在模拟中验证Speckle Flow SIM卡以相干成像，并使用现成的组件构建简单，廉价的实验设置。我们证明，Speckle Flow SIM可以通过可变形运动重建动态场景，而在实验中衍射限制的分辨率为1.88x。
Structured illumination microscopy (SIM) reconstructs a super-resolved image from multiple raw images captured with different illumination patterns; hence, acquisition speed is limited, making it unsuitable for dynamic scenes. We propose a new method, Speckle Flow SIM, that uses static patterned illumination with moving samples and models the sample motion during data capture in order to reconstruct the dynamic scene with super-resolution. Speckle Flow SIM relies on sample motion to capture a sequence of raw images. The spatio-temporal relationship of the dynamic scene is modeled using a neural space-time model with coordinate-based multi-layer perceptrons (MLPs), and the motion dynamics and the super-resolved scene are jointly recovered. We validate Speckle Flow SIM for coherent imaging in simulation and build a simple, inexpensive experimental setup with off-the-shelf components. We demonstrate that Speckle Flow SIM can reconstruct a dynamic scene with deformable motion and 1.88x the diffraction-limited resolution in experiment.
